Shape Anchors for Data-driven Multi-view Reconstruction

Abstract

We present a data-driven method for building dense 3D
reconstructions using a combination of recognition and
multi-view cues. Our approach is based on the idea that
there are image patches that are so distinctive that we
can accurately estimate their latent 3D shapes solely using
recognition. We call these patches shape anchors, and we
use them as the basis of a multi-view reconstruction system
that transfers dense, complex geometry between scenes. We
“anchor” our 3D interpretation from these patches, using
them to predict geometry for parts of the scene that are relatively
ambiguous. The resulting algorithm produces dense
reconstructions from stereo point clouds that are sparse and
noisy, and we demonstrate it on a challenging dataset of
real-world, indoor scenes.